A novel SVM and LOF-based outlier detection routing algorithm for improving the stability period and overall network lifetime of WSN

Wireless sensor network data are frequently erroneous due to inevitable environmental factors like intrusion attacks, signal weakness, and noise, which may vary depending on the situation. Outlier detection, often known as anomaly detection, is a technique for detecting anomalies and recognising noisy data in the aforementioned scenarios. In the proposed work, efforts have been made to design a routing algorithm that can detect anomalies based on LOF and SVM and is more energy-efficient. The primary objective of the proposed algorithm is to design an energy-efficient routing algorithm that is capable of detecting anomalies present in the environment with improved stability period and overall network lifetime. The sensor dataset provided by the Intel Berkeley Research Lab was simulated to assess the suggested approach's efficiency and competency. The simulation results reveal that this identification of anomalous nodes leads to the development of a more energy-efficient routing algorithm with a better stable region and a higher network lifetime. The proposed algorithm gives the best result with LOF. However, SVM with a gamma of 0.0005 could be used successfully in densely deployed wireless sensor networks. The LOF gives a 98% accuracy in finding the anomaly present in the dataset chosen for the simulation.